Homework - EDA Report
Xinyi Zhang
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.4 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 2.0.1 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(ggplot2)
library(dplyr)
library(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinelibrary(ggcorrplot)summarize_numeric = function(dataset){
dataset = select_if(dataset, is.numeric)
summary.table = data.frame(Attribute = names(dataset))
summary.table = summary.table %>%
mutate('Missing values' = apply(dataset, 2,function(x) sum(is.na(x))),
'unique values' = apply(dataset, 2,function(x) length(unique(x))),
'Mean' = colMeans(dataset,na.rm = TRUE),
'Max' = apply(dataset, 2,function(x) max(x,na.rm = TRUE)),
'Min' = apply(dataset, 2,function(x) min(x,na.rm = TRUE)),
'SD' = apply(dataset, 2,function(x) sd(x,na.rm = TRUE))
)
summary.table
}summarize_character = function(dataset){
dataset = select_if(dataset, is.character)
summary.table = data.frame(Attribute = names(dataset))
summary.table = summary.table %>%
mutate('Missing values' = apply(dataset, 2,function(x) sum(is.na(x))),
'unique values' = apply(dataset, 2,function(x) length(unique(x)))
)
summary.table
}1. Variable Identification and Initial Transformations
1.1 Dataset Overview
ER_Admissions = as_tibble(read_csv('ERAdmissions.csv'))## Rows: 161067 Columns: 13## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): Gender, Hospital_Group, Pain_Level, Blood_Pressure_Cat, Reason_For_...
## dbl (7): Length_of_Stay, ICU_Days, Num_Chronic_Conds, Total_Charges, Patient...##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.summary(ER_Admissions)## Length_of_Stay ICU_Days Num_Chronic_Conds Total_Charges
## Min. : 0.0 Min. : 0.00 Min. :-1.000 Min. :-2104
## 1st Qu.: 0.0 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.:22306
## Median : 2.0 Median : 0.00 Median : 1.000 Median :27839
## Mean : 2.9 Mean : 1.48 Mean : 1.017 Mean :28726
## 3rd Qu.: 5.0 3rd Qu.: 2.00 3rd Qu.: 1.000 3rd Qu.:34368
## Max. :32.0 Max. :29.00 Max. : 4.000 Max. :67671
## NA's :23
## Gender Patient_Age Hospital_Group Death
## Length:161067 Min. : 27.00 Length:161067 Min. :0.00000
## Class :character 1st Qu.: 69.00 Class :character 1st Qu.:0.00000
## Mode :character Median : 76.00 Mode :character Median :0.00000
## Mean : 74.44 Mean :0.04095
## 3rd Qu.: 83.00 3rd Qu.:0.00000
## Max. :101.00 Max. :1.00000
##
## Pain_Level Blood_Pressure_Cat Reason_For_Visit Weight_Cat
## Length:161067 Length:161067 Length:161067 Length:161067
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Hospital_Code
## Min. :100002158
## 1st Qu.:325399246
## Median :550656051
## Mean :550224771
## 3rd Qu.:774664008
## Max. :999996959
## 1.2 Numeric Attributes
summarize_numeric(ER_Admissions)## Attribute Missing values unique values Mean Max
## 1 Length_of_Stay 0 29 2.899626e+00 32
## 2 ICU_Days 23 27 1.480397e+00 29
## 3 Num_Chronic_Conds 0 6 1.017428e+00 4
## 4 Total_Charges 0 560 2.872575e+04 67671
## 5 Patient_Age 0 75 7.443534e+01 101
## 6 Death 0 2 4.095190e-02 1
## 7 Hospital_Code 0 161057 5.502248e+08 999996959
## Min SD
## 1 0 4.034192e+00
## 2 0 3.116242e+00
## 3 -1 9.373548e-01
## 4 -2104 8.477141e+03
## 5 27 1.327079e+01
## 6 0 1.981794e-01
## 7 100002158 2.598431e+08Composition: 7 numeric variables.
1.2.1 Data Quality Issues Observed
There are 23 missing values in ICU_Days. Hospital_Code is a unique key. The minimum value of Num_Chronic_Conds and Total_Charges is negative.
1.2.2 Transformations Required
Attribute ‘Death’ should be transformed into factor.
ER_Admissions = ER_Admissions %>% mutate(Death = as.factor(case_when(Death == 0 ~ 'alive', Death == 1 ~ 'dead')))1.2.3 Attributes for Removal
Attribute ‘Hospital Code’ should be removed as it’s a unique key.
ER_Admissions = ER_Admissions[ , !(names(ER_Admissions) %in% 'Hospital_Code')]
ER_Admissions## # A tibble: 161,067 × 12
## Length_of_Stay ICU_Days Num_Chronic_Conds Total_Charges Gender Patient_Age
## <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 0 0 0 15248 M 67
## 2 3 0 2 16082 M 69
## 3 0 0 1 27534 M 84
## 4 9 6 2 26009 M 82
## 5 0 0 2 26009 F 77
## 6 9 6 2 18360 F 72
## 7 0 0 2 18360 M 88
## 8 0 0 0 64263 M 61
## 9 0 0 2 24951 F 73
## 10 3 0 2 24951 F 95
## # … with 161,057 more rows, and 6 more variables: Hospital_Group <chr>,
## # Death <fct>, Pain_Level <chr>, Blood_Pressure_Cat <chr>,
## # Reason_For_Visit <chr>, Weight_Cat <chr>1.3 Character Attributes
summarize_character(ER_Admissions)## Attribute Missing values unique values
## 1 Gender 0 2
## 2 Hospital_Group 0 5
## 3 Pain_Level 0 5
## 4 Blood_Pressure_Cat 11 6
## 5 Reason_For_Visit 0 14
## 6 Weight_Cat 0 4Composition: 6 character variables.
1.3.1 Data Quality Issues Observed
There are 11 missing values in Blood_Pressure_Cat.
1.3.2 Transformations Required
All the character attributes into factors.
ER_Admissions = ER_Admissions %>% mutate(
Gender = as.factor(Gender),
Hospital_Group = as.factor(Hospital_Group),
Pain_Level = as.factor(Pain_Level),
Blood_Pressure_Cat = as.factor(Blood_Pressure_Cat),
Reason_For_Visit = as.factor(Reason_For_Visit),
Weight_Cat = as.factor(Weight_Cat)
)1.3.3 Attributes for Removal
There is no attribute needed to be removed.
1.4 Dataset updates after variabile identification phase
ER_Admissions=na.omit(ER_Admissions)
summary(ER_Admissions)## Length_of_Stay ICU_Days Num_Chronic_Conds Total_Charges Gender
## Min. : 0.0 Min. : 0.00 Min. :-1.000 Min. :-2104 F:91192
## 1st Qu.: 0.0 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.:22306 M:69841
## Median : 2.0 Median : 0.00 Median : 1.000 Median :27839
## Mean : 2.9 Mean : 1.48 Mean : 1.017 Mean :28726
## 3rd Qu.: 5.0 3rd Qu.: 2.00 3rd Qu.: 1.000 3rd Qu.:34368
## Max. :32.0 Max. :29.00 Max. : 4.000 Max. :67671
##
## Patient_Age Hospital_Group Death Pain_Level
## Min. : 27.00 Hosp 1:48333 alive:154438 Mild :24192
## 1st Qu.: 69.00 Hosp 2:15929 dead : 6595 Moderate:48338
## Median : 76.00 Hosp 3:32462 None :32120
## Mean : 74.44 Hosp 4:32235 Severe :40271
## 3rd Qu.: 83.00 Hosp 5:32074 Unknown :16112
## Max. :101.00
##
## Blood_Pressure_Cat Reason_For_Visit Weight_Cat
## Low :15824 Other :44597 Extreme Obese: 9710
## MildlyHigh :53132 Stomach Abdominal Pain:21762 Normal :48517
## ModeratelyHigh:40424 Chest Pain :18465 Obese :49699
## Normal :24202 Back Symptoms :11212 Overweight :53107
## SeverelyHigh :27451 Headache : 9999
## Shortness of Breath : 9777
## (Other) :45221colnames(ER_Admissions %>% select_if(is.factor))## [1] "Gender" "Hospital_Group" "Death"
## [4] "Pain_Level" "Blood_Pressure_Cat" "Reason_For_Visit"
## [7] "Weight_Cat"colnames(ER_Admissions %>% select_if(is.numeric))## [1] "Length_of_Stay" "ICU_Days" "Num_Chronic_Conds"
## [4] "Total_Charges" "Patient_Age"2. Univariate Analysis
2.1 Numeric Attributes
Logical groupings: Patient Situation: Patient_Age, Num_Chronic_Conds Time Measurement: Length_of_Stay, ICU_Days Hospital charge: Total_Charges
h1 = ggplot(ER_Admissions) + geom_histogram(aes(x=Length_of_Stay))
h2 = ggplot(ER_Admissions) + geom_histogram(aes(x=ICU_Days))
h3 = ggplot(ER_Admissions) + geom_histogram(aes(x=Num_Chronic_Conds))
h4 = ggplot(ER_Admissions) + geom_histogram(aes(x=Patient_Age))
h5 = ggplot(ER_Admissions) + geom_histogram(aes(x=Total_Charges)) + ggtitle('Hospital Charges')
grid.arrange(h1, h2, top = 'Time Measurement', ncol=2)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
grid.arrange(h3, h4, top = 'Patient Situation', ncol=2)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
h5## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
2.2 Categorical Attributes
Logical groupings: Patient Demographic: Gender, Reason_For_Visit Patient Situation: Death, Pain_Level, Blood_Pressure_Cat, Weight_Cat Hospital Measurement: Hospital_Group
b1 = ggplot(ER_Admissions) + geom_bar(aes(x=Gender))
b2 = ggplot(ER_Admissions) + geom_bar(aes(x=Hospital_Group)) + ggtitle('Hospital Group')
b3 = ggplot(ER_Admissions) + geom_bar(aes(x=Death))ER_Admissions$Pain_Level = factor(ER_Admissions$Pain_Level, levels = c('None', 'Mild',
'Moderate', 'Severe', 'Unknown'))
b4 = ggplot(ER_Admissions) + geom_bar(aes(x=Pain_Level))ER_Admissions$Blood_Pressure_Cat = factor(ER_Admissions$Blood_Pressure_Cat,
levels = c('Low', 'Normal','MildlyHigh','ModeratelyHigh', 'SeverelyHigh'))
b5 = ggplot(ER_Admissions) + geom_bar(aes(x=Blood_Pressure_Cat)) + theme(axis.text.x=element_text(angle=30,size=6))ER_Admissions$Weight_Cat = factor(ER_Admissions$Weight_Cat,
levels = c('Normal', 'Overweight','Obese','Extreme Obese'))
b6 = ggplot(ER_Admissions) + geom_bar(aes(x=Weight_Cat))b7 = ggplot(ER_Admissions) + geom_bar(aes(x=Reason_For_Visit)) + theme(axis.text.x=element_text(angle=30,size=6))grid.arrange(b1, b7, top = 'Patient Situation', ncol=2)
b2
grid.arrange(b3, b4, b5, b6, top = 'Patient Situation', ncol=2)
3. Bivariate Analysis
3.1 Measure/Measure
ggcorrplot(cor(ER_Admissions %>% select(`Patient_Age`, `Num_Chronic_Conds`, `Length_of_Stay`, `ICU_Days`,
'Total_Charges'))) + ggtitle('Correlation Plot')
age_chron = ggplot(ER_Admissions) + geom_point(aes(x=`Patient_Age`, y = `Num_Chronic_Conds`))
age_stay = ggplot(ER_Admissions) + geom_point(aes(x=`Patient_Age`, y = `Length_of_Stay`))
age_icu = ggplot(ER_Admissions) + geom_point(aes(x=`Patient_Age`, y = `ICU_Days`))
age_charge = ggplot(ER_Admissions) + geom_point(aes(x=`Patient_Age`, y = `Total_Charges`))
chron_stay = ggplot(ER_Admissions) + geom_point(aes(x=`Num_Chronic_Conds`, y = `Length_of_Stay`))
chron_icu = ggplot(ER_Admissions) + geom_point(aes(x=`Num_Chronic_Conds`, y = `ICU_Days`))
chron_charge = ggplot(ER_Admissions) + geom_point(aes(x=`Num_Chronic_Conds`, y = `Total_Charges`))
stay_icu = ggplot(ER_Admissions) + geom_point(aes(x=`Length_of_Stay`, y = `ICU_Days`))
stay_charge = ggplot(ER_Admissions) + geom_point(aes(x=`Length_of_Stay`, y = `Total_Charges`))
icu_charge = ggplot(ER_Admissions) + geom_point(aes(x=`ICU_Days`, y = `Total_Charges`)) grid.arrange(age_chron, age_stay, age_icu, age_charge, chron_stay, chron_icu, nrow = 3)
grid.arrange(chron_charge, stay_icu, stay_charge, icu_charge, nrow = 2)
3.2 Category/Category
c1 = ggplot(ER_Admissions) + geom_count(aes(x= `Gender`, y = `Death`))
c2 = ggplot(ER_Admissions) + geom_count(aes(x= `Gender`, y = `Hospital_Group`))
c3 = ggplot(ER_Admissions) + geom_count(aes(x= `Gender`, y = `Weight_Cat`))
c4 = ggplot(ER_Admissions) + geom_count(aes(x= `Gender`, y = `Blood_Pressure_Cat`))
c5 = ggplot(ER_Admissions) + geom_count(aes(x= `Gender`, y = `Pain_Level`))
c6 = ggplot(ER_Admissions) + geom_count(aes(x= `Gender`, y = `Reason_For_Visit`)) + theme(axis.text.x=element_text(size=4))
c7 = ggplot(ER_Admissions) + geom_count(aes(x= `Reason_For_Visit`, y = `Hospital_Group`)) + theme(axis.text.x=element_text(angle=30,size=6))
c8 = ggplot(ER_Admissions) + geom_count(aes(x= `Death`, y = `Hospital_Group`))
c9 = ggplot(ER_Admissions) + geom_count(aes(x= `Weight_Cat`, y = `Blood_Pressure_Cat`)) + theme(axis.text.x=element_text(angle=30,size=6))
c10 = ggplot(ER_Admissions) + geom_count(aes(x= `Death`, y = `Pain_Level`)) grid.arrange(c1, c2, c3, c4, c5, c6, top = 'Gender Related', nrow = 3)
grid.arrange(c8, c9, c10, top = 'Patient Situation Realted', ncol = 2)
c7
f1 = ggplot(ER_Admissions) + geom_bar(aes(x=Gender, fill = Death), position = "fill") + labs(y = "Percent")
f2 = ggplot(ER_Admissions) + geom_bar(aes(x=Gender, fill = Hospital_Group), position = "fill") + labs(y = "Percent")
f3 = ggplot(ER_Admissions) + geom_bar(aes(x=Gender, fill = Weight_Cat), position = "fill") + labs(y = "Percent")
f4 = ggplot(ER_Admissions) + geom_bar(aes(x=Gender, fill = Blood_Pressure_Cat), position = "fill") + labs(y = "Percent")
f5 = ggplot(ER_Admissions) + geom_bar(aes(x=Gender, fill = Pain_Level), position = "fill") + labs(y = "Percent")
f6 = ggplot(ER_Admissions) + geom_bar(aes(x=Gender, fill = Reason_For_Visit), position = "fill") + labs(y = "Percent")grid.arrange(f1, f2, f3, f4, ncol = 2)
grid.arrange(f5, f6, ncol = 2)
f7 = ggplot(ER_Admissions) + geom_bar(aes(x=Death, fill = Gender), position = "fill") + labs(y = "Percent")
f8 = ggplot(ER_Admissions) + geom_bar(aes(x=Death, fill = Reason_For_Visit), position = "fill") + labs(y = "Percent")
f9 = ggplot(ER_Admissions) + geom_bar(aes(x=Death, fill = Weight_Cat), position = "fill") + labs(y = "Percent")
f10 = ggplot(ER_Admissions) + geom_bar(aes(x=Death, fill = Blood_Pressure_Cat), position = "fill") + labs(y = "Percent")
f11 = ggplot(ER_Admissions) + geom_bar(aes(x=Death, fill = Pain_Level), position = "fill") + labs(y = "Percent")
f12 = ggplot(ER_Admissions) + geom_bar(aes(x=Death, fill = Hospital_Group), position = "fill") + labs(y = "Percent")grid.arrange(f7, f8, f9, f10, ncol = 2)
grid.arrange(f11, f12, ncol = 2)
f13 = ggplot(ER_Admissions) + geom_bar(aes(x=Hospital_Group, fill = Gender), position = "fill") + labs(y = "Percent")
f14 = ggplot(ER_Admissions) + geom_bar(aes(x=Hospital_Group, fill = Reason_For_Visit), position = "fill") + labs(y = "Percent")
f15 = ggplot(ER_Admissions) + geom_bar(aes(x=Hospital_Group, fill = Weight_Cat), position = "fill") + labs(y = "Percent")
f16 = ggplot(ER_Admissions) + geom_bar(aes(x=Hospital_Group, fill = Blood_Pressure_Cat), position = "fill") + labs(y = "Percent")
f17 = ggplot(ER_Admissions) + geom_bar(aes(x=Hospital_Group, fill = Pain_Level), position = "fill") + labs(y = "Percent")
f18 = ggplot(ER_Admissions) + geom_bar(aes(x=Hospital_Group, fill = Death), position = "fill") + labs(y = "Percent")grid.arrange(f13, f14, f15, f16, ncol = 2)
grid.arrange(f17, f18, ncol = 2)
f19 = ggplot(ER_Admissions) + geom_bar(aes(x=Weight_Cat, fill = Gender), position = "fill") + labs(y = "Percent")
f20 = ggplot(ER_Admissions) + geom_bar(aes(x=Weight_Cat, fill = Reason_For_Visit), position = "fill") + labs(y = "Percent")
f21 = ggplot(ER_Admissions) + geom_bar(aes(x=Weight_Cat, fill = Hospital_Group), position = "fill") + labs(y = "Percent")
f22 = ggplot(ER_Admissions) + geom_bar(aes(x=Weight_Cat, fill = Blood_Pressure_Cat), position = "fill") + labs(y = "Percent")
f23 = ggplot(ER_Admissions) + geom_bar(aes(x=Weight_Cat, fill = Pain_Level), position = "fill") + labs(y = "Percent")
f24 = ggplot(ER_Admissions) + geom_bar(aes(x=Weight_Cat, fill = Death), position = "fill") + labs(y = "Percent")grid.arrange(f19, f20, f21, f22, ncol = 2)
grid.arrange(f23, f24, ncol = 2)
f25 = ggplot(ER_Admissions) + geom_bar(aes(x=Blood_Pressure_Cat, fill = Gender), position = "fill") + labs(y = "Percent")
f26 = ggplot(ER_Admissions) + geom_bar(aes(x=Blood_Pressure_Cat, fill = Reason_For_Visit), position = "fill") + labs(y = "Percent")
f27 = ggplot(ER_Admissions) + geom_bar(aes(x=Blood_Pressure_Cat, fill = Hospital_Group), position = "fill") + labs(y = "Percent")
f28 = ggplot(ER_Admissions) + geom_bar(aes(x=Blood_Pressure_Cat, fill = Weight_Cat), position = "fill") + labs(y = "Percent")
f29 = ggplot(ER_Admissions) + geom_bar(aes(x=Blood_Pressure_Cat, fill = Pain_Level), position = "fill") + labs(y = "Percent")
f30 = ggplot(ER_Admissions) + geom_bar(aes(x=Blood_Pressure_Cat, fill = Death), position = "fill") + labs(y = "Percent")grid.arrange(f25, f26, f27, f28, ncol = 2)
grid.arrange(f29, f30, ncol = 2)
f31 = ggplot(ER_Admissions) + geom_bar(aes(x=Pain_Level, fill = Gender), position = "fill") + labs(y = "Percent")
f32 = ggplot(ER_Admissions) + geom_bar(aes(x=Pain_Level, fill = Reason_For_Visit), position = "fill") + labs(y = "Percent")
f33 = ggplot(ER_Admissions) + geom_bar(aes(x=Pain_Level, fill = Hospital_Group), position = "fill") + labs(y = "Percent")
f34 = ggplot(ER_Admissions) + geom_bar(aes(x=Pain_Level, fill = Weight_Cat), position = "fill") + labs(y = "Percent")
f35 = ggplot(ER_Admissions) + geom_bar(aes(x=Pain_Level, fill = Blood_Pressure_Cat), position = "fill") + labs(y = "Percent")
f36 = ggplot(ER_Admissions) + geom_bar(aes(x=Pain_Level, fill = Death), position = "fill") + labs(y = "Percent")grid.arrange(f31, f32, f33, f34, ncol = 2)
grid.arrange(f35, f36, ncol = 2)
f37 = ggplot(ER_Admissions) + geom_bar(aes(x=Reason_For_Visit, fill = Gender), position = "fill") + labs(y = "Percent")
f38 = ggplot(ER_Admissions) + geom_bar(aes(x=Reason_For_Visit, fill = Pain_Level), position = "fill") + labs(y = "Percent")
f39 = ggplot(ER_Admissions) + geom_bar(aes(x=Reason_For_Visit, fill = Hospital_Group), position = "fill") + labs(y = "Percent")
f40 = ggplot(ER_Admissions) + geom_bar(aes(x=Reason_For_Visit, fill = Weight_Cat), position = "fill") + labs(y = "Percent")
f41 = ggplot(ER_Admissions) + geom_bar(aes(x=Reason_For_Visit, fill = Blood_Pressure_Cat), position = "fill") + labs(y = "Percent")
f42 = ggplot(ER_Admissions) + geom_bar(aes(x=Reason_For_Visit, fill = Death), position = "fill") + labs(y = "Percent")grid.arrange(f37, f38, f39, f40, ncol = 2)
grid.arrange(f41, f42, ncol = 2)
3.3 Category/Measure
bx1 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Gender`, y= `Patient_Age`))
bx2 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Death`, y= `Patient_Age`))
bx3 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Hospital_Group`, y= `Patient_Age`))
bx4 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Pain_Level`, y= `Patient_Age`))
bx5 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Blood_Pressure_Cat`, y= `Patient_Age`)) + theme(axis.text.x=element_text(angle=30,size=6))
bx6 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Weight_Cat`, y= `Patient_Age`))
bx7 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Reason_For_Visit`, y= `Patient_Age`)) + theme(axis.text.x=element_text(angle=30,size=6))grid.arrange(bx1, bx2, bx3, bx4, top = 'Patient Age by Category', ncol = 2) 
grid.arrange(bx5, bx6, bx7, top = 'Patient Age by Category', ncol = 2)
bx11 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Gender`, y= `Num_Chronic_Conds`))
bx21 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Death`, y= `Num_Chronic_Conds`))
bx31 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Hospital_Group`, y= `Num_Chronic_Conds`))
bx41 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Pain_Level`, y= `Num_Chronic_Conds`))
bx51 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Blood_Pressure_Cat`, y= `Num_Chronic_Conds`)) + theme(axis.text.x=element_text(angle=30,size=6))
bx61 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Weight_Cat`, y= `Num_Chronic_Conds`))
bx71 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Reason_For_Visit`, y= `Num_Chronic_Conds`)) + theme(axis.text.x=element_text(angle=30,size=6))grid.arrange(bx11, bx21, bx31, bx41, top = 'Number of Chronic Conditions by Category', ncol = 2) 
grid.arrange(bx51, bx61, bx71, top = 'Number of Chronic Conditions by Category', ncol = 2) 
bx12 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Gender`, y= `Length_of_Stay`))
bx22 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Death`, y= `Length_of_Stay`))
bx32 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Hospital_Group`, y= `Length_of_Stay`))
bx42 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Pain_Level`, y= `Length_of_Stay`))
bx52 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Blood_Pressure_Cat`, y= `Length_of_Stay`)) + theme(axis.text.x=element_text(angle=30,size=6))
bx62 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Weight_Cat`, y= `Length_of_Stay`))
bx72 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Reason_For_Visit`, y= `Length_of_Stay`)) + theme(axis.text.x=element_text(angle=30,size=6))grid.arrange(bx12, bx22, bx32, bx42, top = 'Length of Stay by Category', ncol = 2) 
grid.arrange(bx52, bx62, bx72, top = 'Length of Stay by Category', ncol = 2) 
bx13 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Gender`, y= `ICU_Days`))
bx23 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Death`, y= `ICU_Days`))
bx33 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Hospital_Group`, y= `ICU_Days`))
bx43 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Pain_Level`, y= `ICU_Days`))
bx53 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Blood_Pressure_Cat`, y= `ICU_Days`)) + theme(axis.text.x=element_text(angle=30,size=6))
bx63 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Weight_Cat`, y= `ICU_Days`))
bx73 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Reason_For_Visit`, y= `ICU_Days`)) + theme(axis.text.x=element_text(angle=30,size=6))grid.arrange(bx13, bx23, bx33, bx43, top = 'ICU Days by Category', ncol = 2) 
grid.arrange(bx53, bx63, bx73, top = 'ICU Days by Category', ncol = 2) 
bx14 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Gender`, y= `Total_Charges`))
bx24 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Death`, y= `Total_Charges`))
bx34 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Hospital_Group`, y= `Total_Charges`))
bx44 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Pain_Level`, y= `Total_Charges`))
bx54 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Blood_Pressure_Cat`, y= `Total_Charges`)) + theme(axis.text.x=element_text(angle=30,size=6))
bx64 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Weight_Cat`, y= `Total_Charges`))
bx74 = ggplot(ER_Admissions) + geom_boxplot(aes(x = `Reason_For_Visit`, y= `Total_Charges`)) + theme(axis.text.x=element_text(angle=30,size=6))grid.arrange(bx14, bx24, bx34, bx44, top = 'Total_Charges by Category', ncol = 2) 
grid.arrange(bx54, bx64, bx74, top = 'Total_Charges by Category', ncol = 2) 
ggplot(ER_Admissions) + geom_histogram(aes(x=Patient_Age)) + facet_wrap(~ Reason_For_Visit, nrow=2)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(ER_Admissions) + geom_histogram(aes(x=Patient_Age)) + facet_wrap(~ Pain_Level, nrow=2)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(ER_Admissions) + geom_histogram(aes(x=Patient_Age)) + facet_wrap(~ Blood_Pressure_Cat, nrow=2)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(ER_Admissions) + geom_histogram(aes(x=Patient_Age)) + facet_wrap(~ Weight_Cat, nrow=2)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(ER_Admissions) + geom_histogram(aes(x=Num_Chronic_Conds)) + facet_wrap(~ Death)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(ER_Admissions) + geom_histogram(aes(x=Num_Chronic_Conds)) + facet_wrap(~ Pain_Level, nrow=2)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
4. Final Analytic-Ready Table Description
Numeric(5): Patient Demographic: Patient_Age Patient Situation: Num_Chronic_Conds Hospital Measurement: ICU_Days, Length_of_Stay Hospital Charge: Total_Charge
Factors(7): Patient Demographic: Gender, Reason_For_Visit Patient Situation: Death, Pain_Level, Blood_Pressure_Cat, Weight_Cat Hospital Measurement: Hospital_Group